In spectral (or multi-energy) imaging one can reconstruct for physical object properties of interest such as photo effect, Compton scattering, water content, bone content, iodine content, etc. But due to the fact that for many spectral tomographic X-ray setups the overlap of the X-ray spectra used for acquisition is quite large, the reconstruction problem is ill posed and this may lead to the occurrence of, on occasion severe, noise in the resulting images. In order to combat noise build-up, iterative reconstruction for spectral X-ray tomography is of increasing interest. In iterative reconstruction noise can be reduced by giving more weight to data with good signal-to-noise ratio, but also by applying regularization functions. These regularization functions allow incorporating a priori information about the object into the reconstruction. In most cases they enforce smoothness (to reduce noise) or similarity to a given image.
As mentioned above, with spectral X-ray tomography it is possible to reconstruct physical properties of the object. Of great interest in this context is for instance the spatial distribution of a certain material of interest to so construct so called material maps. A non-limiting example is for instance “contrast agent maps” such as “iodine maps”. Such contrast agents are administered before or during imaging to increase image contrast for soft tissue for instance. However, even with iterative reconstruction, there are still cases where such reconstructed material maps are of comparably poor quality.